{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "\n",
    "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (10, 5)})\n",
    "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.set_loglevel(\"WARNING\")\n",
    "mpl.rcParams[\"lines.markersize\"] = 8\n",
    "\n",
    "now = datetime.datetime.now()\n",
    "now_str = now.strftime(\"%m_%d_%H_%M\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_barplot(data, df, df_random_baseline=None, save_path=None):\n",
    "    \n",
    "    fig, axs = plt.subplots(1, 3, figsize=(10, 3))\n",
    "    fig.suptitle(f'Policy performance on the {data} set', y=1.05)\n",
    "\n",
    "    # Goal achieved\n",
    "    sns.barplot(data=df[df['dataset'] == data], errorbar=\"sd\", x='train_dataset_size', y='goal_achieved', ax=axs[0])\n",
    "    axs[0].set_ylim(0, 1)\n",
    "    axs[0].grid(True, alpha=0.3)\n",
    "\n",
    "    # Collided\n",
    "    sns.barplot(data=df[df['dataset'] == data], errorbar=\"sd\", x='train_dataset_size', y='collided', ax=axs[1])\n",
    "    axs[1].grid(True, alpha=0.3)\n",
    "\n",
    "    # Off road\n",
    "    sns.barplot(data=df[df['dataset'] == data], errorbar=\"sd\", x='train_dataset_size', y='off_road', ax=axs[2])\n",
    "    axs[2].grid(True, alpha=0.3)\n",
    "        \n",
    "    # Random policy performance\n",
    "    if df_random_baseline is not None:\n",
    "        rand_pi_mean_gr = df_random_baseline[df_random_baseline['dataset'] == data]['goal_achieved'].mean()\n",
    "        rand_pi_mean_col = df_random_baseline[df_random_baseline['dataset'] == data]['collided'].mean()\n",
    "        rand_pi_mean_off = df_random_baseline[df_random_baseline['dataset'] == data]['off_road'].mean()\n",
    "        \n",
    "        axs[0].axhline(rand_pi_mean_gr, color='r', linestyle='--', linewidth=2)\n",
    "        axs[1].axhline(rand_pi_mean_col, color='r', linestyle='--', linewidth=2)\n",
    "        axs[2].axhline(rand_pi_mean_off, color='r', linestyle='--', linewidth=2, label='Random policy')\n",
    "        \n",
    "        axs[1].set_ylim(0, rand_pi_mean_col + 0.05)\n",
    "        axs[2].set_ylim(0, rand_pi_mean_off + 0.05)\n",
    "        \n",
    "    fig.legend()\n",
    "    plt.tight_layout()\n",
    "\n",
    "    sns.despine()\n",
    "    if save_path:\n",
    "        plt.savefig(save_path)\n",
    "        \n",
    "        \n",
    "def make_histplot(data, df, bins=None, save_path=None):\n",
    "    \n",
    "    fig, axs = plt.subplots(1, 3, figsize=(10, 3))\n",
    "    fig.suptitle(f'Policy performance on the {data} set', y=1.05)\n",
    "\n",
    "    # Goal achieved\n",
    "    sns.histplot(data=df[df['dataset'] == data], x='goal_achieved', bins=bins, ax=axs[0])\n",
    "    axs[0].grid(True, alpha=0.3)\n",
    "\n",
    "    # Collided\n",
    "    sns.histplot(data=df[df['dataset'] == data], x='collided', bins=bins, ax=axs[1])\n",
    "    axs[1].grid(True, alpha=0.3)\n",
    "\n",
    "    # Off road\n",
    "    sns.histplot(data=df[df['dataset'] == data], x='off_road', bins=bins, ax=axs[2])\n",
    "    axs[2].grid(True, alpha=0.3)\n",
    "        \n",
    "    fig.legend()\n",
    "    plt.tight_layout()\n",
    "\n",
    "    sns.despine()\n",
    "    if save_path:\n",
    "        plt.savefig(save_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BASE_DIR = \"...\"\n",
    "FIGURE_DIR = \"...\"\n",
    "\n",
    "# Constants\n",
    "GRID_ALPHA = 0.4\n",
    "ERRORBAR = 'ci'\n",
    "DATA = 'test'\n",
    "\n",
    "colors = sns.color_palette(\"tab10\").as_hex()\n",
    "blue = colors[0]\n",
    "orange = colors[1]\n",
    "green = colors[2]\n",
    "red = colors[3]\n",
    "purple = colors[4]\n",
    "\n",
    "SCENE_BASED_METRICS = ['goal_achieved_frac', 'collided_frac', 'off_road_frac', 'other_frac']\n",
    "AGENT_BASED_METRICS = ['goal_achieved_count', 'collided_count', 'off_road_count', 'other_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "['#1f77b4',\n",
       " '#ff7f0e',\n",
       " '#2ca02c',\n",
       " '#d62728',\n",
       " '#9467bd',\n",
       " '#8c564b',\n",
       " '#e377c2',\n",
       " '#7f7f7f',\n",
       " '#bcbd22',\n",
       " '#17becf']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(\"tab10\").as_hex()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling laws dataframes\n",
    "df_100 = pd.read_csv(BASE_DIR + 'model_PPO__R_100__02_23_21_28_06_645_006500.csv')\n",
    "df_1000 = pd.read_csv(BASE_DIR + 'model_PPO__R_1000__02_23_22_34_47_084_007500.csv')\n",
    "df_10_000 = pd.read_csv(BASE_DIR + 'model_PPO____R_10000__02_26_11_02_54_752_002800.csv')\n",
    "\n",
    "#df_rand_baseline = pd.read_csv(BASE_DIR + \"random_baseline.csv\")\n",
    "\n",
    "# Best model\n",
    "df_10_000 = pd.read_csv(BASE_DIR + \"model_PPO__C__R_10000__01_28_20_57_35_873_011426.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregate performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tab_rand_baseline = df_rand_baseline.groupby('dataset')[['goal_achieved', 'collided', 'off_road']].agg(['mean', 'std'])\n",
    "# tab_rand_baseline = tab_rand_baseline * 100  \n",
    "# tab_rand_baseline = tab_rand_baseline.round(2)  \n",
    "\n",
    "# tab_rand_baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">goal_achieved_frac</th>\n",
       "      <th colspan=\"2\" halign=\"left\">collided_frac</th>\n",
       "      <th colspan=\"2\" halign=\"left\">off_road_frac</th>\n",
       "      <th colspan=\"2\" halign=\"left\">other_frac</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <td>99.81</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.44</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.31</td>\n",
       "      <td>2.59</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        goal_achieved_frac       collided_frac       off_road_frac        \\\n",
       "                      mean   std          mean   std          mean   std   \n",
       "dataset                                                                    \n",
       "test                 99.81  1.53          0.44  3.17          0.31  2.59   \n",
       "\n",
       "        other_frac       \n",
       "              mean  std  \n",
       "dataset                  \n",
       "test          0.14  1.1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_10_000 = df_10_000[df_10_000['controlled_agents_in_scene'] != 0]\n",
    "\n",
    "tab_10_000 = df_10_000.groupby('dataset')[SCENE_BASED_METRICS].agg(['mean', 'std'])\n",
    "tab_10_000 = tab_10_000 * 100  \n",
    "tab_10_000 = tab_10_000.round(2)  \n",
    "\n",
    "tab_10_000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>goal_achieved_count</th>\n",
       "      <th>collided_count</th>\n",
       "      <th>off_road_count</th>\n",
       "      <th>other_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <td>99.81</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         goal_achieved_count  collided_count  off_road_count  other_count\n",
       "dataset                                                                  \n",
       "test                   99.81            0.32            0.18         0.17"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the sum of controlled agents\n",
    "df = df_10_000[df_10_000['dataset'] == 'test']\n",
    "\n",
    "total_controlled_agents = df['controlled_agents_in_scene'].sum()\n",
    "\n",
    "# Group by 'dataset' and compute normalized values\n",
    "tab_agent_based_10_000 = (df.groupby('dataset')[AGENT_BASED_METRICS].sum() / total_controlled_agents)*100\n",
    "\n",
    "tab_agent_based_10_000.round(2)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Error distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.959999999999999"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_error = df_10_000[df_10_000['dataset'] == 'test']\n",
    "\n",
    "df_error = df_10_000[\n",
    "    (df_10_000['collided_frac'] > 0) |\n",
    "    (df_10_000['off_road_frac'] > 0) |\n",
    "    (df_10_000['other_frac'] > 0)\n",
    "    \n",
    "]\n",
    "\n",
    "(df_error.shape[0] / df_10_000.shape[0]) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scene</th>\n",
       "      <th>goal_achieved_count</th>\n",
       "      <th>goal_achieved_frac</th>\n",
       "      <th>collided_count</th>\n",
       "      <th>collided_frac</th>\n",
       "      <th>off_road_count</th>\n",
       "      <th>off_road_frac</th>\n",
       "      <th>other_count</th>\n",
       "      <th>other_frac</th>\n",
       "      <th>controlled_agents_in_scene</th>\n",
       "      <th>episode_lengths</th>\n",
       "      <th>dataset</th>\n",
       "      <th>model_name</th>\n",
       "      <th>train_dataset_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tfrecord-00048-of-00150_243.json</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>test</td>\n",
       "      <td>model_PPO__C__R_10000__01_28_20_57_35_873_011426</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>tfrecord-00029-of-00150_80.json</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>test</td>\n",
       "      <td>model_PPO__C__R_10000__01_28_20_57_35_873_011426</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>tfrecord-00096-of-00150_283.json</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>37.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>test</td>\n",
       "      <td>model_PPO__C__R_10000__01_28_20_57_35_873_011426</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>tfrecord-00061-of-00150_178.json</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>test</td>\n",
       "      <td>model_PPO__C__R_10000__01_28_20_57_35_873_011426</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>tfrecord-00115-of-00150_93.json</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>test</td>\n",
       "      <td>model_PPO__C__R_10000__01_28_20_57_35_873_011426</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               scene  goal_achieved_count  goal_achieved_frac  \\\n",
       "4   tfrecord-00048-of-00150_243.json                  3.0            1.000000   \n",
       "8    tfrecord-00029-of-00150_80.json                 44.0            1.000000   \n",
       "15  tfrecord-00096-of-00150_283.json                 36.0            0.972973   \n",
       "16  tfrecord-00061-of-00150_178.json                  5.0            0.833333   \n",
       "24   tfrecord-00115-of-00150_93.json                 16.0            0.941176   \n",
       "\n",
       "    collided_count  collided_frac  off_road_count  off_road_frac  other_count  \\\n",
       "4              0.0       0.000000             1.0       0.333333          0.0   \n",
       "8              1.0       0.022727             0.0       0.000000          0.0   \n",
       "15             0.0       0.000000             0.0       0.000000          1.0   \n",
       "16             0.0       0.000000             0.0       0.000000          1.0   \n",
       "24             0.0       0.000000             1.0       0.058824          0.0   \n",
       "\n",
       "    other_frac  controlled_agents_in_scene  episode_lengths dataset  \\\n",
       "4     0.000000                         3.0             44.0    test   \n",
       "8     0.000000                        44.0             27.0    test   \n",
       "15    0.027027                        37.0             90.0    test   \n",
       "16    0.166667                         6.0             90.0    test   \n",
       "24    0.000000                        17.0             90.0    test   \n",
       "\n",
       "                                          model_name  train_dataset_size  \n",
       "4   model_PPO__C__R_10000__01_28_20_57_35_873_011426               10000  \n",
       "8   model_PPO__C__R_10000__01_28_20_57_35_873_011426               10000  \n",
       "15  model_PPO__C__R_10000__01_28_20_57_35_873_011426               10000  \n",
       "16  model_PPO__C__R_10000__01_28_20_57_35_873_011426               10000  \n",
       "24  model_PPO__C__R_10000__01_28_20_57_35_873_011426               10000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_error.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(896, 14)"
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_error.shape"
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  {
   "cell_type": "code",
   "execution_count": 13,
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   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(9, 3), sharex=True)\n",
    "metrics = ['collided_frac', 'off_road_frac', 'other_frac']\n",
    "titles = ['Collided in scene', 'Off-road in scene', 'Other in scene']\n",
    "colors = [red, orange, 'grey']\n",
    "\n",
    "for ax, metric, title, color in zip(axes, metrics, titles, colors):\n",
    "    sns.histplot(df_error, stat='percent', color=color, x=metric, kde=False, bins=10, ax=ax)\n",
    "    #ax.set_title(title, fontsize=14)\n",
    "    ax.set_xlabel('')\n",
    "    ax.grid(True, alpha=0.2)\n",
    "    ax.set_ylim(0, 100)\n",
    "    ax.set_xlabel(title, fontsize=12)\n",
    "    sns.despine()\n",
    "\n",
    "# Improve layout\n",
    "#plt.xlabel(\"Scene-based metric (%)\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "sns.despine()\n",
    "fig.savefig(f'{FIGURE_DIR}eror_distribution_{now_str}.pdf', bbox_inches='tight', format='pdf');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr\n",
    "\n",
    "# Compute correlation and p-value\n",
    "corr, p_value = pearsonr(df_error['collided_frac'], df_error['off_road_frac'])\n",
    "\n",
    "print(f'Correlation: {corr:.4f}')\n",
    "print(f'P-value: {p_value:.4f}')\n",
    "\n",
    "alpha = 0.05  # Typical significance level\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
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   "source": [
    "# THE_TABLE = tab_train_10_000_cont\n",
    "\n",
    "# # Combine mean and std into \"mean ± std\"\n",
    "# def format_mean_std(row):\n",
    "#     formatted = {}\n",
    "#     for col in row.index.get_level_values(0).unique():  # Iterate over the metric names\n",
    "#         mean = row[(col, 'mean')]\n",
    "#         std = row[(col, 'std')]\n",
    "#         formatted[col] = f\"{mean:.2f} ± {std:.2f}\"\n",
    "#     return formatted\n",
    "\n",
    "# # Apply formatting to all rows\n",
    "# tab_formatted = THE_TABLE.apply(format_mean_std, axis=1)\n",
    "\n",
    "# # Flatten the result\n",
    "# tab_formatted = pd.DataFrame(tab_formatted.tolist(), index=tab_formatted.index)\n",
    "\n",
    "# # Generate LaTeX table\n",
    "# latex_table = tab_formatted.to_latex(\n",
    "#     index=True,\n",
    "#     caption=\"Aggregate performance on WOMD (mean ± std)\",\n",
    "#     label=\"tab:aggregate_performance\",\n",
    "# )\n",
    "\n",
    "# # Generate LaTeX table code with \\resizebox to fit within a single column\n",
    "# latex_code = \"\"\"\n",
    "# \\\\begin{table}[htbp]\n",
    "# \\\\centering\n",
    "# \\\\resizebox{\\\\columnwidth}{!}{\n",
    "# \\\\begin{tabular}{@{}lcccc@{}} \n",
    "# \\\\toprule\n",
    "# \\\\textbf{Dataset} & \\\\textbf{Goal achieved} & \\\\textbf{Collided} & \\\\textbf{Off-road} & \\\\textbf{Other} \\\\\\\\\n",
    "# \\\\midrule\n",
    "# \"\"\"\n",
    "\n",
    "# # Add rows to the table\n",
    "# for idx, row in tab_formatted.iterrows():\n",
    "#     latex_code += f\"\\\\textbf{{{idx}}} & \"  # dataset name in bold\n",
    "#     latex_code += \" & \".join([f\"{value}\" for value in row])  # values for each metric\n",
    "#     latex_code += \" \\\\\\\\ \\n\"\n",
    "\n",
    "# # Closing part of the LaTeX table\n",
    "# latex_code += \"\"\"\n",
    "# \\\\bottomrule\n",
    "# \\\\end{tabular}\n",
    "# }\n",
    "# \\\\caption{Aggregate performance on train and 10,000 test traffic scenarios from the Waymo Open Motion Dataset (mean $\\\\pm$ std)}\n",
    "# \\\\label{tab:aggregate_perf_best_policy}\n",
    "# \\\\end{table}\n",
    "# \"\"\"\n",
    "# print(latex_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Scaling laws\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df_100, df_1000, df_10_000]).reset_index(drop=True)\n",
    "categories = ['goal_achieved_frac','collided_frac', 'off_road_frac']\n",
    "df[categories] = df[categories] * 100\n",
    "df['train_dataset_size'] = df['train_dataset_size'].apply(lambda x: f\"{int(x):,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ERRORBAR = 'se'\n",
    "MARKERSIZE = 6\n",
    "MARKER = 's'\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(9.3, 3))\n",
    "\n",
    "# Goal achieved\n",
    "sns.pointplot(\n",
    "    data=df[df['dataset'] == DATA], \n",
    "    errorbar=ERRORBAR, \n",
    "    x='train_dataset_size', \n",
    "    y='goal_achieved_frac', \n",
    "    marker='o',\n",
    "    markersize=MARKERSIZE,\n",
    "    color=blue, \n",
    "    ax=axs[0]\n",
    ")\n",
    "axs[0].set_ylim(70, 105)\n",
    "axs[0].grid(axis='y', linestyle='-', color='lightgrey', alpha=0.3)\n",
    "axs[0].set_xlabel(\"Training dataset size\", labelpad=10)\n",
    "# Add horizontal line at 100%\n",
    "axs[0].axhline(100, color='#aac0ea', linestyle='--', linewidth=2, zorder=0)\n",
    "axs[0].set_ylabel(\"Goal achieved [%]\")\n",
    "\n",
    "# Collided\n",
    "sns.pointplot(\n",
    "    data=df[df['dataset'] == DATA], \n",
    "    errorbar=ERRORBAR, \n",
    "    x='train_dataset_size', \n",
    "    y='collided_frac', \n",
    "    marker='s',\n",
    "    markersize=MARKERSIZE,\n",
    "    color=red, \n",
    "    ax=axs[1]\n",
    ")\n",
    "\n",
    "axs[1].set_ylim(-2, 15)\n",
    "axs[1].grid(axis='y', linestyle='-', color='lightgrey', alpha=0.3)\n",
    "axs[1].axhline(0, color='#ffc7c4', linestyle='--', linewidth=2, zorder=0)\n",
    "axs[1].set_xlabel(\"Training dataset size\", labelpad=10)\n",
    "axs[1].set_ylabel(\"Collided [%]\")\n",
    "\n",
    "# Off road\n",
    "sns.pointplot(\n",
    "    data=df[df['dataset'] == DATA], \n",
    "    errorbar=ERRORBAR, \n",
    "    x='train_dataset_size', \n",
    "    y='off_road_frac', \n",
    "    marker=\"D\",\n",
    "    markersize=MARKERSIZE,\n",
    "    color=orange, \n",
    "    ax=axs[2]\n",
    ")\n",
    "\n",
    "axs[2].set_ylim(-2, 15)\n",
    "axs[2].grid(axis='y', linestyle='-', color='lightgrey', alpha=0.3)\n",
    "axs[2].set_xlabel(\"Training dataset size\", labelpad=10)\n",
    "axs[2].axhline(0, color=\"#ffb995\", linestyle='--', linewidth=2, zorder=0)\n",
    "axs[2].set_ylabel(\"Off-road [%]\")\n",
    "\n",
    "plt.tight_layout()\n",
    "sns.despine()\n",
    "plt.savefig(f'{FIGURE_DIR}scaling_laws_point_{now_str}_simple.pdf', bbox_inches='tight', format='pdf')"
   ]
  }
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